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Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. [ABSTRACT FROM AUTHOR]

We recently demonstrated that electronically constructed family pedigrees (e-pedigrees) have great value in epidemiologic research using electronic health record (EHR) data. Prior to this work, it has been well accepted that family health history is a major predictor for a wide spectrum of diseases, reflecting shared effects of genetics, environment, and lifestyle. With the widespread digitalization of patient data via EHRs, there is an unprecedented opportunity to use machine learning algorithms to better predict disease risk. Although predictive models have previously been constructed for a few important diseases, we currently know very little about how accurately the risk for most diseases can be predicted. It is further unknown if the incorporation of e-pedigrees in machine learning can improve the value of these models. In this study, we devised a family pedigree-driven high-throughput machine learning pipeline to simultaneously predict risks for thousands of diagnosis codes using thousands of input features. Models were built to predict future disease risk for three time windows using both Logistic Regression and XGBoost. For example, we achieved average areas under the receiver operating characteristic curves (AUCs) of 0.82, 0.77 and 0.71 for 1, 6, and 24 months, respectively using XGBoost and without e-pedigrees. When adding e-pedigree features to the XGBoost pipeline, AUCs increased to 0.83, 0.79 and 0.74 for the same three time periods, respectively. E-pedigrees similarly improved the predictions when using Logistic Regression. These results emphasize the potential value of incorporating family health history via e-pedigrees into machine learning with no further human time., (©2024 AMIA - All rights reserved.)